Impact of telemedicine on diagnosis, clinical management and outcomes in rural trauma patients: A rapid review
Bibliographic record
Abstract
INTRODUCTION: Rural trauma patients are at increased risk of morbidity and mortality compared to trauma patients treated in urban facilities. Factors contributing to this disparity include differences in resource availability and increased time to definitive treatment for rural patients. Telemedicine can improve the early management of these patients by enabling rural providers to consult with trauma specialists at urban centres. The purpose of this study was to assess the impact of telemedicine utilisation on the diagnosis, clinical management and outcomes of rural trauma patients. MATERIALS AND METHODS: June 2018. Manual searches were also conducted in relevant systematic reviews, key journals and bibliographies of included studies. RESULTS: The literature search identified 187 articles, of which 8 articles were included in the review. All 8 studies reported on clinical management, while the impact of telemedicine use on diagnosis and outcomes was reported in 4 and 5 studies, respectively. Study findings suggest that the use of telemedicine may improve patient diagnosis, streamline the process of transferring patients and reduce length of stay. Use of telemedicine had minimal impact on mortality and complications in rural trauma patients. CONCLUSIONS: The evidence identified by this rapid review suggests that telemedicine may improve the diagnosis, management and outcomes of rural trauma patients. Further research is required to validate these findings by performing large and well-designed studies in rural areas, ideally as randomised clinical trials.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".